Methods and apparatus to determine efficiencies of media delivery across platforms

ABSTRACT

Methods and apparatus to determine efficiencies of media delivery across platforms are disclosed. An example method includes measuring, with a sensor, first neuro-response data from a person when exposed to media output via a first media delivery platform type. Measuring second neuro-response data from the person when exposed to media output via a second media delivery platform type, different than the first media delivery platform type. Accessing a first and second performance metric for the respective media delivery platform type, the performance metrics based on a first and second reach of the respective media delivery platform types with respect to a target group of audience members. Accessing a first and second effectiveness metric for the respective media delivery platform types, the effectiveness metric based on the respective neuro-response data. Rating at least one of the first or second media delivery platform types based on the first and the second platform metrics.

This patent arises from a continuation of U.S. patent application Ser.No. 13/837,148, filed Mar. 15, 2013, entitled “Methods And Apparatus ToDetermine Efficiencies Of Media Delivery Across Platforms,” now U.S.Pat. No. ______. The U.S. patent application Ser. No. 13/837,148 ishereby incorporated herein by reference in its entirety

FIELD OF THE DISCLOSURE

This disclosure relates generally to media delivery and, moreparticularly, to methods and apparatus to determine efficiencies ofmedia delivery across platforms.

BACKGROUND

Media delivery, such as the presentation of programs and advertisements,has expanded from platforms such as stationary television and stationaryradio to online (e.g., Internet-based) and mobile (e.g., cell phone orother portable device) delivery. Different platforms are accessed bydifferent numbers and demographics of individuals.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example system constructed in accordancewith the teachings of this disclosure to determine an effectiveness of amulti-platform media campaign.

FIG. 2 is a block diagram of an example platform effectivenessmeasurement system to implement the effectiveness determiner of FIG. 1.

FIG. 3 is a table illustrating example calculations of effectiveness ofa media campaign for multiple platforms.

FIG. 4 is a table illustrating example calculations of costeffectiveness of a media campaign for multiple platforms.

FIG. 5 is a flowchart representative of example computer readableinstructions which, when executed, cause a computer to implement theexample system of FIG. 1 to determine platform metrics.

FIG. 6 is a flowchart representative of example computer readableinstructions which, when executed, cause a computer to implement theexample system of FIG. 1 to determine platform performance per unit costfor a media campaign.

FIG. 7 is a flowchart representative of example computer readableinstructions which, when executed, cause a computer to implement theexample system of FIG. 1 to estimate performance of a media campaign.

FIG. 8 is a flowchart representative of example computer readableinstructions which may be executed to analyze EEG data collected fromthe example headset and implemented by the example effectivenessdeterminer of FIG. 2.

FIG. 9 is a block diagram of an example processor platform capable ofexecuting the instructions of FIGS. 5, 6, 7, and/or 8 to implement thesystems of FIGS. 1 and/or 2.

Wherever possible, the same reference numbers will be used throughoutthe drawing(s) and accompanying written description to refer to the sameor like parts.

DETAILED DESCRIPTION

Audience measurement providers, such as The Nielsen Company, measureaudience reach and calculate ratings based on multiple platforms (e.g.,television, radio, mobile, online, outdoor, print media, etc.). Eachmedia platform (e.g., television, radio, mobile, online, outdoor, printmedia, etc.) has norms (e.g., baselines of scores and/or ratings forspecific types of media (e.g., content and/or advertisements). Suchnorms are actionable thresholds of success. For example, televisionratings have specific baselines, by demography and/or genre of content,for what is considered a good, successful, and/or popular show.Advertisers desire to understand the cumulative effects and the relativeimpacts of media campaigns (e.g., advertising, programming) when acampaign is launched that uses one or more media platforms. For example,advertisers may desire to know whether one platform is more influentialthan another platform for a given campaign.

While TV ratings are a standardized currency in the televisionadvertising market, platforms such as mobile and online ratings do notclearly relate to TV ratings using known measurements as a directcomparison. Furthermore, ratings based on different platforms may havedifferent respective levels of granularity and/or quality, such as alevel of noise in the data, comprehensiveness of normative comparisons,and/or reliability of market performance predictability on differentplatforms. For example, TV rating predictions are considered morereliable than predictions of online or mobile ratings. Thus, comparisonsbetween these platforms are difficult.

Example methods and apparatus disclosed herein enable effectivecomparison and rationalization of performance (e.g., ratings) betweendifferent distribution platforms, such as television, radio, online,mobile, outdoor, and/or print media, among others. Comparison and/orrationalization enabled by example methods and apparatus disclosedherein provide a holistic view of the returns of a campaign acrossmultiple platforms and can be used to adjust future media campaignsbased on the performances for, for example, specific demographies and/orgeographies on a platform-by-platform basis. Example methods andapparatus disclosed herein enable the use of cross-platform performancedeterminations and/or comparisons to improve (e.g., optimize) selectionand/or spending across multiple platforms for a media campaign.

Example methods and apparatus disclosed herein determine neurologicaleffectiveness or efficiency measurements for each platform, subplatform,and/or target group to be measured. Example effectiveness or efficiencymeasurements may be representative of persuasion, purchase intent,attention, emotion, memory, and/or fluency.

As used herein, the term “persuasion” refers to a medium's attempt togenerate an intent to behave in a particular manner (e.g., to purchase aparticular good or service). As used herein, the term “effectiveness”refers to an ability or propensity of media to achieve a desired effectin an individual (e.g., to create a lasting memory, to change a rate ofmedia consumption, to influence a purchasing decision, etc.). As usedherein, an “effectiveness metric” refers to a composite measure ofcognitive processing (e.g., a weighted combination of attention,emotional engagement, and memory activation). As used herein, the term“engagement” or “emotional engagement” refers to a measure of intensityof emotional response and/or automatic motivational classification ofstimuli (e.g., non-conscious classification of sensory experiences aspotentially rewarding (approach motivation) or potentially threatening(avoidance motivation)). As used herein, the term “attention” refers toa measure of sustained focus and/or shifts in focus over time. As usedherein, the term “fluency” refers to a measure of an audience member'sability to understand the media to which he or she is exposed. As usedherein, the term “reach” refers to the unduplicated audience of one ormore media platforms. As used herein, the term “performance metric” is ameasure of any result of interest associated with media, a platform, ora combination of media on a platform. In some examples disclosed herein,a media impact performance metric is a product of an effectivenessmeasurement and a reach measurement. As used herein, the term“subplatform” refers to a subset of media presentation types that canoccur and be classified as occurring on a platform. As used herein, theterm “platform” is generic to platforms and subplatforms. As usedherein, the term “target group” refers to a group of people of interestwho share at least one characteristic, such as age, gender, or incomerange.

As used herein, the efficiency, effectiveness, and/or performancemetrics, may be measures corresponding to specific media when deliveredvia a specific platform (and/or subplatform). Additionally oralternatively, the efficiency, effectiveness, and/or performance metricsmay be measures for a corresponding platform (and/or subplatform)without reference to any particular media. For example, the efficiency,effectiveness, and/or performance of a plurality of media delivered viaa platform (and/or subplatform) may be generalized to the platform(and/or subplatform).

Example methods and apparatus disclosed herein obtain reach measurementsfor TV, online, mobile, and/or other platforms of interest. Examplemethods and apparatus disclosed herein combine respective effectivenessmeasurements and reach measurements for each platform to obtain anestimated media impact performance metric of the platform. Some examplemethods and apparatus determine a product (e.g., multiplication) of theeffectiveness metric with the reach to determine the media impactperformance metric. In some examples, in addition to platform metrics(e.g., performance metrics for a platform as a whole), performancemetrics are determined for subplatforms and/or target groups on aplatform or subplatform.

FIG. 1 is a block diagram of an example system 100 constructed inaccordance with the teachings of this disclosure to determine aneffectiveness of a multi-platform media campaign. The example system 100of FIG. 1 includes an audience measurement system 102, an effectivenessdeterminer 104, and a platform metric calculator 106. The exampleaudience measurement system 102 of FIG. 1 determines audienceinformation, such as reach, for each of multiple media platforms (e.g.,television, online, mobile devices, outdoor, radio, print media, etc.).Media platforms are also referred to herein as simply “platforms.”

In some examples, the audience measurement system 102 of FIG. 1determines the audience information for each of the example platforms bytarget group. Target groups may be defined based on one or a combinationof age, gender, demographic group, socioeconomic status, geographiclocation, personal interests, size of household, and/or other criteria.Accordingly, a given target group (e.g., males, age 24-35) may overlapwith some target groups (e.g., persons in a household with$60,000-$99,999 annual income, persons located on the east coast) whilebeing mutually exclusive with other target groups (e.g., females, age24-35, versus females, age 36-45).

The example audience measurement system 102 of FIG. 1 includes atelevision audience measurement system 108, an online audiencemeasurement system 110, a mobile audience measurement system 112, anoutdoor audience measurement system 114, a radio audience measurementsystem 116, and a print media audience measurement system 118. However,alternative and/or additional systems may be used. For example, more,fewer, and/or different systems may be employed.

The example television audience measurement system 108 of FIG. 1determines reach for one or more target groups and/or for a totalpopulation. Additionally, the example television audience measurementsystem 108 determines reach for one or more subplatforms (e.g., localstations, broadcasting networks, cable and/or satellite channels,pay-per-view channels, time-shifted viewing, space-shifted viewing,etc.) of the television platform. The precise methodologies and/orstructures of the television audience measurement system 108 of FIG. 1are irrelevant to this disclosure. The example television audiencemeasurement system 108 of FIG. 1 may be implemented, for example,according to any system or combination(s) of systems described in U.S.Pat. Nos. 5,481,294, 5,771,307, 5,550,928, 6,647,548, 7,239,9817,640,141, and/or 8,359,610, all of which are hereby incorporated byreference in their entireties.

The example online audience measurement system 110 of FIG. 1 determinesreach for one or more target groups and/or for a total population.Additionally, the example online audience measurement system 110determines reach for one or more subplatforms (e.g., web sites, webpages, online services, streaming media providers, peer-to-peernetworks, downloaded media, etc.) of the online platform. The precisemethodologies and/or structures of the online audience measurementsystem 110 of FIG. 1 are irrelevant to this disclosure. The exampleonline audience measurement system 110 may be implemented, for example,according to any system or combination(s) of systems described in U.S.Pat. Nos. 5,675,510, 6,108,637, and/or 6,327,619, all of which arehereby incorporated by reference in their entireties.

The example mobile audience measurement system 112 of FIG. 1 determinesreach for one or more target groups and/or for a total population.Additionally, the example mobile audience measurement system 112determines reach for one or more subplatforms (e.g., mobile networkcarriers, mobile handset manufacturers, mobile handset softwareproviders, mobile handset models, mobile applications, etc.) of themobile platform. The precise methodologies and/or structures of themobile audience measurement system 112 of FIG. 1 are irrelevant to thisdisclosure. The example mobile audience measurement system 112 may beimplemented, for example, according to any system or combination(s) ofsystems described in U.S. Pat. Nos. 5,675,510 and/or 6,108,637, and/orU.S. patent Pre-Grant Publication No. 2012/0151079, all of which arehereby incorporated by reference in their entireties.

The example outdoor audience measurement system 114 of FIG. 1 determinesreach for one or more target groups and/or for a total population.Additionally, the example outdoor audience measurement system 114determines reach for one or more subplatforms (e.g., billboards,locations, outdoor advertising companies and/or networks, mobile outdooradvertising such as advertising trucks, taxis, and/or airplane banners,etc.) of the outdoor platform. The precise methodologies and/orstructures of the outdoor audience measurement system 114 of FIG. 1 areirrelevant to this disclosure. The example outdoor audience measurementsystem 114 may be implemented, for example, according to any system orcombination(s) of systems described in U.S. patent Pre-Grant PublicationNo. 2008/0243573 and/or in U.S. patent application Ser. No. 13/793,771,filed on Mar. 11, 2013, and entitled “Methods and Apparatus to MeasureExposure to Mobile Advertisements,” all which are hereby incorporated byreference in their entireties.

The example radio audience measurement system 116 of FIG. 1 determinesreach for one or more target groups and/or for a total population.Additionally, the example radio audience measurement system 116determines reach for one or more subplatforms (e.g., terrestrialbroadcast radio, Internet radio, satellite radio, high definition radio,particular radio channels, particular radio networks, etc.) of the radioplatform. The precise methodologies and/or structures of the radioaudience measurement system 116 of FIG. 1 are irrelevant to thisdisclosure. The example radio audience measurement system 116 may beimplemented, for example, according to any system or combination(s) ofsystems described in U.S. Pat. Nos. 5,481,294, 7,239,981, and/or7,640,141, all of which are hereby incorporated by reference in theirentireties.

The example print media audience measurement system 118 of FIG. 1determines reach for one or more target groups and/or for a totalpopulation. Additionally, the example print media audience measurementsystem 118 determines reach for one or more subplatforms (e.g.,magazines, newspapers, periodicals, books, etc.) of the print mediaplatform. The precise methodologies and/or structures of the print mediaaudience measurement system 118 of FIG. 1 are irrelevant to thisdisclosure. The example print media audience measurement system 118 maybe implemented, for example, according to any system or combination(s)of systems described in U.S. Pat. Nos. 4,992,867 and 8,368,918, all ofwhich are hereby incorporated by reference in their entireties.

While examples of various audience measurement systems are providedabove, the example television audience measurement system 108, theexample online audience measurement system 110, the example mobileaudience measurement system 112, the example outdoor audiencemeasurement system 114, the example radio audience measurement system116, the example print media audience measurement system 118 and/or,more generally, the example audience measurement system 102 of FIG. 1may be implemented using any past, present, and/or future audiencemeasurement system(s) and/or improvement(s) thereof, and/or anycombination(s) of audience measurement system(s) and/or improvement(s).

The example platform metric calculator 106 obtains (e.g., receives)effectiveness metrics from the effectiveness determiner 104. In theexample of FIG. 1, obtaining the first effectiveness metric for thefirst media on the first platform includes measuring neurologicalactivity of a person representative of the target group of audiencemembers while displaying the first media to the person on the firstplatform. The example effectiveness determiner 104 of FIG. 1 determinesthe effectiveness of media platforms for presenting media to targetgroups. For example, the effectiveness determiner 104 may determine aneffectiveness or efficiency of a platform (e.g., of media presented on aplatform) on a target group as a percentage of the target group as ameasure of the degree to which the group in question is engaged withand/or attentive to media when exposed to the media via the platform.

In some examples, the effectiveness determiner 104 of FIG. 1 measuresneurological and/or neurophysiological responses of subjects whenexposed to media (which may include, for example, programs and/oradvertising of interest). In some examples, the effectiveness determiner104 measures neurological and/or neurophysiological responses byexposing a first person representative of the target group of audiencemembers to media and determining at least one of: engagement, attention,memory, persuasion, effectiveness, emotion, or purchase intent of thefirst person. The example effectiveness determiner 104 of FIG. 1determines the effectiveness of respective platforms by measuring one ormore of engagement, attention, memory, persuasion, emotion, and/orpurchase intent of human subjects before, during, and/or after exposureto media of interest presented on the platforms of interest. Based onthe measured effects on the people, the example effectiveness determiner104 calculates the effectiveness of the platform. In the example of FIG.1, the effectiveness or efficiency represents a percentage or proportionof persons who are affected (e.g., who are persuaded, whose behavior ismodified, whose attention, emotional engagement, and/or memory retentionare achieved, etc.) by the media or platform. An example equation tocalculate effectiveness is shown below.

$\begin{matrix}{E = \frac{A_{E}}{A_{T}}} & {{Equation}\mspace{14mu} (1)}\end{matrix}$

In Equation (1), E is the effectiveness, A_(E) is the number of peopleaffected to a threshold degree, and A_(T) is the total audience size.However, other measures of effectiveness or efficiency may be used. Anexample system to measure the effectiveness of one or more platforms isdescribed below with reference to FIG. 2.

In some examples, the effectiveness determiner 104 of FIG. 1 determinesthe effectiveness of one or more platforms using surveys,questionnaires, focus groups, and/or any other manual research and/orautomatic data collection method. For example, a human surveyor maypresent media of interest (with or without presenting other media formasking or anti-bias measures) to one or more people via a platform ofinterest. The human surveyor then asks questions of the people exposedto the media of interest and/or platform of interest to obtain dataregarding their engagement, attention, memory, persuasion, emotion,and/or purchase intent with respect to the media of interest and/ormaterial in the media of interest. The resulting data may be manuallyprovided (e.g., via an input device) to the example effectivenessdeterminer 104 for aggregation, classification, and/or processing. Insome examples, the effectiveness determiner 104 administers onlinesurveys or surveys directly to mobile devices and/or meters. Examplesystems to implement the effectiveness determiner 104 using surveys isdescribed in U.S. patent application Ser. No. 12/263,079, filed on Oct.31, 2008, and entitled “Methods and Apparatus to Perform ConsumerSurveys,” the entirety of which is hereby incorporated by reference.

The example effectiveness determiner 104 of FIG. 1 outputs aneffectiveness metric for each platform of interest. The effectivenessmetric for a platform represents a fraction or proportion of people(overall and/or in one or more target groups) who are affected by mediapresented by the platform. The effectiveness determiner 104 maydetermine effectiveness metrics generally for a platform and/orsubplatform, and/or for a platform and/or subplatform with respect to atarget group and/or with respect to media of interest for presentationvia the platform.

The example platform metric calculator 106 of FIG. 1 obtains (e.g.,receives, accesses from storage, etc.) the reach of media platforms(e.g., from the audience measurement system(s) 102, 108-118) and obtains(e.g., receives, accesses from storage, etc.) the respectiveeffectiveness of the media delivered via the platforms (e.g., from theeffectiveness determiner 104). The example platform metric calculator106 includes a performance estimator 120, a performance per unit costestimator 122, and a platform selector 124.

The example performance estimator 120 of FIG. 1 estimates (e.g.,calculates) the performance of media on different platforms based on thereach and the effectiveness of the platforms. The performance estimator120 further calculates the performance of the platforms for specifiedtarget groups, which may be received from, for example, an advertiser orresearcher. In the illustrated example, the performance estimator 120calculates a media impact performance metric (I) for a given platform(P) by calculating the product of the effectiveness metric for thecorresponding media with the reach of the corresponding platform inaccordance with the following equation.

I _(P) =E*R _(P)  Equation (2)

In Equation (2), I_(P) is the media impact performance metric for theplatform P, E is the effectiveness metric of the media, and R_(P) is thereach of the platform P.

The example performance per unit cost estimator 122 of FIG. 1 obtainsthe media impact performance estimates for the various platforms understudy from the performance estimator 120 and obtains costs to presentmedia via the platforms of interest (e.g., the platforms for whichperformance is estimated). Based on the media impact performance metricand the respective cost, the example performance per unit cost estimator122 estimates the platform performance of each platform (e.g., platformof interest) per unit cost (e.g., performance per dollar or othercurrency of interest, such as Euros, etc.). In the illustrated example,the platform performance (PP_(P)) for a given platform (P) per unit costmay be calculated in accordance with the following formula.

$\begin{matrix}{{PP}_{P} = \frac{I_{P}}{C_{P}}} & {{Equation}\mspace{14mu} (3)}\end{matrix}$

In equation (3), I_(P) is the media impact performance metric for theplatform P, and C is the cost (e.g., in US dollars) to purchase 1 GRP(gross ratings point) worth of exposure on the corresponding platform P.

The example platform selector 124 of FIG. 1 selects one or moreplatforms and/or media (e.g., media to be delivered on a selectedplatform) for investment in advertising and/or programming. In theexample of FIG. 1, the platform selector 124 obtains the platformperformance per unit cost estimates from the performance per unit costestimator 122, obtains a budget (e.g., an advertising campaign budget),and/or obtains a target performance. The platform selector 124 mayselect one or more platforms and/or subplatforms for use in advertisingbased on the budget, performance, and/or performance-per-unit-cost. Forexample, the platform selector 124 may make budget-restricted platformselection decisions to increase (e.g., maximize) the advertising impactachieved by spending the received budget. In some other examples, theplatform selector 124 may make a performance-driven platform selectiondecision by decreasing (e.g., minimizing) a cost to obtain a thresholdperformance for an advertising campaign. The two example platformselection decisions (e.g., budget-restricted, performance-driven)illustrate example priorities to be used in selecting platforms and/ormedia for an advertising campaign.

Additionally or alternatively, the example platform selector 124calculates a mix (e.g., an optimized mix) of media, platforms, and/orsubplatforms based on the performance, the performance-per-unit-cost,and/or one or more constraints. For example, platform(s) and/orsubplatform(s) may be constrained to particular audience sizes (e.g.,minimum audiences per platform, minimum target group reach per platform,etc.) and/or costs (e.g., maximum campaign cost, maximum spending perplatform or subplatform) to diversify an advertising campaign and avoidinvesting all of a campaign's budget into a single, highest-performanceplatform and/or subplatform. Such diversification can increase theeffectiveness (e.g., success) of an advertising campaign due to the factthat different platforms and/or subplatforms can have significantnon-overlapping audiences. Example constraints include lower and/orupper audience (e.g., reach) limits per platform and/or subplatform,lower and/or upper cost limits per platform and/or subplatform, and/orany other constraints or limits that may be placed on a per-platformand/or per-subplatform basis. In some examples, constraints on costsand/or audience may be placed on media to be presented via the platformsand/or subplatforms. The example platform selector 124 selects one ormore media to be presented on platform(s) and/or subplatform(s) (e.g.,an optimized set of media, platform(s), and/or subplatform(s) asdetermined by reach, cost, performance, and/orperformance-per-unit-cost) based on audience and/or cost limits placedon the media, platform(s), and/or subplatform(s). Other uses are alsopossible.

The example audience measurement system 102, the example effectivenessdeterminer 104, and/or the example platform metric calculator 106 maydetermine reach, effectiveness, and/or platform metrics (e.g.,performance, performance-per-unit-cost, etc.) for any marketing mix,advertising campaign strategy, and/or any other platform selectioncriteria. For example, selection criteria for distinguishing media,selecting platforms, and/or forming an advertising campaign may includetime(s) of day of media presentation, target demographic groups (e.g.,ages targets, gender targets, geographic targets, etc.), media genres(e.g., comedic programming or advertising, emotional advertising,children's advertising, etc.), advertising strategy (e.g., direct appealadvertising, informational advertising), and/or any other criteria.Accordingly, the example system 100 of FIG. 1 may provide advertiserswith cross-platform performance metrics for multiple dimensions and/orvariables. For example, the calculations represented by equations(1)-(3) above may be performed for each of a number of platforms andtheir values compared and/or combined to determine cross-platformmetrics.

FIG. 2 is a block diagram of an example implementation of theeffectiveness determiner 104 of FIG. 1. The effectiveness determiner 104may be used for determining, processing and/or evaluating a user'sattention to media to determine a measure of the effectiveness of themedia. The example effectiveness determiner 104 of the illustratedexample includes a headset 202, which may include, for example, one ormore headset(s) disclosed in U.S. patent application Ser. Nos.13/728,900; 13/728,913; and 13/730,212 and U.S. Provisional PatentApplication Ser. No. 61/684,640, all of which are incorporated herein byreference in their entireties. In addition, the headset 202 may beimplemented for example, with the effectiveness determiner 104 ofFIG. 1. The headset 202 processes electroencephalographic (EEG) signalsand/or other sensor data to develop a picture of a mental state of auser including, for example, an emotional state, a state of engagement,a state of attention and/or any other neurological state. As disclosedbelow, the example effectiveness determiner 104 of FIG. 2 may be used todetermine if the user is paying attention to a media program, todetermine where a user's eyes are focused, what emotional and/or mentalstate the user is experiencing and/or for other applications. In theillustrated example effectiveness determiner 104, the headset 202includes analyzer components including an EEG sensor 204 such as, forexample, one or a plurality of electrode(s), a program identifier 206,an eye tracker sensor 208, an accelerometer 210, an attention evaluator212, a database 214 and a transmitter 216. The analyzer components204-216 are communicatively coupled via a communication link 218. Theanalyzer components 204-216 may be, for example, incorporated into orotherwise supported by the headset 202 such as, for example, in acompartment on a headset.

The EEG sensor 204 includes a plurality of individual electrodes todetect electrical activity along the scalp of a user. This data may beused to determine attention, memory, focus and/or other neurologicalstates. The example eye tracker sensor 208 is used to track eye movementand/or the direction in which a user's eyes are directed. For example,the eye tracker sensor 208 may be a camera or other sensor that isincorporated into an appendage that extends from the headset 202 and isdirected to one or both of the user's eyes. In other examples, the eyetracker sensor 208 may be a camera or other sensor on or near acomputer, a television, a mobile phone screen or other location togather data related to the user's eye movement. The eye tracker sensor208 may continuously record what the user is seeing. In some examples,the eye tracker sensor 208 is placed around the middle of the user'seyebrows. Also, in some examples, the eye tracker sensor includes amonocular or binocular (e.g., one eye or two eye coverage) infra-red(IR) camera to track the pupil and/or corneal reflection positions toaide in determining a point of regard of the user's viewpoint. In someexamples, the eye tracker sensor 208 incorporates and/or is used inconjunction with an accelerometer/attitude measurement system 210.Mobile eye-tracking systems that are mounted to a user's head aresusceptible to erroneous measurements as the subject moves his or herhead relative to the position he or she had during calibration of thesystem. The example accelerometer 210 continuously tracks the movementrelative to the calibration position, which enables adjustment of theeye tracking data to thereby enhance the accuracy of the point-of-regardmeasurement from the eye-tracking sensor 208.

The eye track data may be synchronized with and/or otherwise used tocorroborate the EEG data or otherwise may be used in conjunction withthe EEG to determine a neurological state of the user. Eye movementsprovide a target of a user's attention allocation. For example, if theuser is looking in the direction of a television and his or her EEG dataindicates that he or she is in a state of engagement or attention, theeye track data and EEG data together demonstrate that the attention waslikely directed to the television.

The example system of FIG. 2 also includes database 214 for localstorage of raw data, processed data, result data, history logs,programming data from a media source, and/or any other type of data. Thetransmitter 216 of the illustrated example communicates the data at anystage of processing and/or the results of the analysis from the headset202 to a remote data facility 220, as disclosed in more detail below.

In some example implementations, the effectiveness determiner 104 isprovided with a program identifier 206 to collect audience measurementdata. The example effectiveness determiner 104 determines if a user'sneurological state indicates that the user is focused (e.g., engagedwith the media) while watching a certain media. The program identifier206 identifies media to which the user is exposed. The programidentification can be done with any technology, for example, the programcan be identified by collecting audio codes and/or signatures using amicrophone on the headset 202 to collect audio signals as disclosed inThomas, U.S. Pat. No. 5,481,294, which is incorporated by referenceherein in its entirety. The program identifier 206 collects dataconcerning the media, such as, for example, a television show, anadvertisement, a movie, a news clip, radio program, a web page, or anyother media and identifies the media (e.g., content or advertisement)based on the collected data and/or forwards the collected data toanother device to perform the identification.

In the collection of audience measurement data, the exampleeffectiveness determiner 104 gathers EEG data from the EEG sensors 204of the headset 202. The effectiveness determiner 104 gathers eyetracking data from the eye tracking sensor 208 to determine whichdirection the user is gazing during the media broadcast. The attentionevaluator 212 uses data from the EEG sensor 204 and the eye trackersensor 208 to determine if a user is paying attention to the media. Forexample, if the EEG sensors 204 detect brain waves (e.g., electricalactivity) indicative of increased thought, and the eye tracking sensor208 determines that the user is looking at the TV, the attentionevaluator 212 will output a signal (e.g., an effectiveness metric) thatthe user is focused and immersed in that particular media program beingbroadcast. However, if the program identifier 206 determines a certainprogram is being presented, and the EEG sensors 204 indicate decreasingbrain activity, or if the eye tracker sensor 208 determines the user isnot looking at the TV, then the attention evaluator 212 will output asignal (e.g., an effectiveness metric) that the user is not focused orimmersed on that particular media program.

To determine user emotional and/or mental state based on the EEG data,the attention evaluator 212 analyzes the EEG data to evaluate brainactivity in particular frequency bands of the EEG data and/or inparticular regions of the brain. For example, EEG data can be classifiedin various bands. Brainwave frequencies include delta, theta, alpha,beta and gamma frequency ranges. Delta waves are classified as thoseless than about 4 Hertz (Hz) and are prominent during sleep. Theta waveshave frequencies between about 3.5 Hz to about 7.5 Hz and are associatedwith memories, attention, emotions, and sensations. Theta waves aretypically prominent during states of internal focus. Alpha frequenciesreside between about 7.5 Hz and about 13 Hz and typically peak around 10Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between about 14 Hz and about 30 Hz. Beta wavesare prominent during states of motor control, long range synchronizationbetween areas, analytical problem solving, judgment, and decisionmaking. Gamma waves occur between about 30 Hz and about 100 Hz and areinvolved in binding of different populations of neurons together into anetwork for the purpose of carrying out a certain cognitive or motorfunction, as well as in attention and memory. Because the skull anddermal layers attenuate waves in this frequency range, brain waves aboveabout 75 Hz (e.g., high gamma band or kappa band) are less easilymeasured than waves in lower frequency bands. Assessments and/orcalculations of the relationship(s) and correlation(s) of the frequencybands and regions of activity of the EEG data are used to determine anemotional or mental state of a person including, for example, attention,emotional engagement, memory or resonance, etc.

For example, the regions of brain activity, the interaction betweenregions of brain activity, and/or the interactions including couplingsbetween frequency bands signify particular mental states. Also,inter-regional coherencies of frequency bands as measured from gainand/or phase may be used to estimate the effectiveness of media inevoking a desired response (e.g., attention) in a subject. In addition,inter-hemispheric measurement, asymmetry in one or more frequency bands,asymmetry in inter-regional intra-hemispheric coherence and/or asymmetryin inter-regional intra-hemispheric inter-frequency coupling may be usedto measure of emotional engagement.

For example, the attention evaluator 212 may be used to determine orcalculate an interaction between a first frequency band of the EEG dataand a second frequency band of the EEG by detecting a first pattern ofoscillation in the first frequency band, detecting a second pattern ofoscillation in the second frequency band and identifying a degree ofphase synchrony between the first pattern and the second pattern. Themedia effectiveness evaluation, in this example, is based on the degreeof phase synchrony.

In other example, the attention evaluator 212 detects a first pattern ofoscillation in a first frequency band of EEG data and detects a secondpattern of oscillation in a second frequency band of the EEG data. Theattention evaluator 212 identifies a degree of phase synchrony betweenthe first pattern from the first frequency band and the second patternfrom the second frequency band by detecting a repeating sequence ofrelative phase angles between the first pattern of oscillation in thefirst frequency band and the second pattern of oscillation in the secondfrequency band. The media effectiveness evaluation, in this example, isbased on the degree of the phase synchrony at a specific point in time.

In other example, the attention evaluator 212 analyzes EEG data todetermine effectiveness data for media based on a degree of asymmetrybetween a first frequency band of the EEG data for measured in a firsthemisphere of a brain of a panelist and a second frequency band of theEEG data measured in a second hemisphere of the brain. The degree ofasymmetry is identified by detecting a first amplitude of the firstfrequency band and detecting a second amplitude of the second frequencyband. The attention evaluator 212 compares the first amplitude and thesecond amplitude to determine a difference between the first amplitudeof the first frequency band and the second amplitude of the secondfrequency band. Also, the attention evaluator 212 assigns the degree ofasymmetry to the relationship between the first frequency band and thesecond frequency band based on the difference between the firstamplitude of the first frequency band and the second amplitude of thesecond frequency band. Thus, in this example, the effectiveness of themedia is based on a degree of inter-frequency, inter-hemisphericasymmetry, which is identified by comparing the amplitudes of twofrequency bands from different hemispheres.

In another example, the attention evaluator 212 analyzes an interactionbetween a first frequency band of EEG data and a second frequency bandof EEG by calculating a degree of phase synchrony or amplitudesynchrony. The phase synchrony or amplitude synchrony is determined bydetecting a first pattern of oscillation in the first frequency band anddetecting a second pattern of oscillation in the second frequency band.In addition, the attention evaluator 212 detects a repeating sequence ofphase angles or relative amplitude between the first pattern ofoscillation in the first frequency band and the second pattern ofoscillation in the second frequency band. The effectiveness of the media(e.g., a determined effectiveness metric) is based on the interaction.

In still another example, the attention evaluator 212 assesses theeffectiveness of media based on a first asymmetry between two amplitudesfrom two frequency bands and a second asymmetry between two differentamplitudes of the frequency bands. Specifically, in this example, theattention evaluator 212 identifies a first asymmetry in two frequencybands of EEG data related to a first portion of the media. The firstasymmetry identified by comparing a first amplitude of the firstfrequency band and a second amplitude of the second frequency band todetermine a first difference between the first amplitude of the firstfrequency band and the second amplitude of the second frequency band. Inaddition, a first value is assigned to the first asymmetry based on thefirst difference between the first amplitude of the first frequency bandand the second amplitude of the second frequency band. The attentionevaluator 212 also identifies a second asymmetry in two frequency bandsof EEG data related to a second portion of the media. The first andsecond portions of the media may be temporally disparate portions of themedia or different portions that are concurrently experienced by thepanelist (e.g., video and audio). The second asymmetry is identified bycomparing a third amplitude of the first frequency band and a fourthamplitude of the second frequency band to determine a second differencebetween the third amplitude of the first frequency band and the fourthamplitude of the second frequency band. A second value is assigned tothe second asymmetry based on the second difference between the thirdamplitude of the first frequency band and the fourth amplitude of thesecond frequency band. The attention evaluator 212 assess aneffectiveness of the media for each of the first and second portionsbased on the first value of the first asymmetry and the second value ofthe second asymmetry.

Data reflected of the user paying attention, the user not payingattention, the user in a state of semi-involvement with the program, orother user mental state, and the identity of the program are storable inthe database 214 and transmittable by the transmitter 216 to an outputincluding, for example, the remote data facility 220. Raw data,processed data, a history log or an indicator of audience measurementalso may be transmitted to the remote data facility 220 for collection.The remote data facility 220 may be, for example, a marketing company, abroadcast company, an entertainment studio, a television network and/orany other organization that might benefit from or otherwise desire toknow when users are and/or are not focused on broadcast programs andwhat those programs are. In some examples, the headset 202 iscommunicatively coupled to the remote data facility 220 via acommunication channel 224 such as common telephone line, a landline, aninternet connection, radio waves, and/or any other communicationtechnology capable of sending signals. This example allows broadcastingcompanies and/or marketing personnel to analyze which programs peopleare watching, when they are watching the programs and/or when they arefocused during the broadcast.

Though the examples disclosed above are described in relation to theexample headset 202, one or more of the analysis components may belocated off of a headset. For example, the attention evaluator 212 mayanalysis EEG gathered from a headset, but the attention evaluator 212 isincorporated into a separate device such as, for example, a desktopcomputer, a laptop computer, a tablet, etc.

While an example implementation of the effectiveness determiner 104 ofFIG. 1 is described in FIG. 2, other systems may be used to implementall or part of the effectiveness determiner 104. Examples of systemsthat may be used are disclosed in U.S. Pat. Nos. 6,099,319 and8,230,457, all of which are hereby incorporated by reference in theirentireties, among others.

FIG. 3 is a table 300 illustrating example calculations of effectivenessof a media campaign for multiple platforms. The example table 300 ofFIG. 3 may be generated and/or populated by the example audiencemeasurement systems 102, 108-118, the example effectiveness determiner104, and/or the example performance estimator 120 of FIG. 1. The exampletable 300 of FIG. 3 may be stored in a computer readable storage mediumsuch as a memory (e.g., the memories 914, 916 of FIG. 9) or a massstorage device (e.g., the mass storage device 928 of FIG. 9). Otherexamples of storage devices that may be used to store the table 300 aredescribed below with reference to FIG. 9.

The example table 300 provides values for the performance metric 302(e.g., the media impact performance metric I_(P) of equations (2) and/or(3)) for combination(s) of designated platforms 304, subplatforms 306,and/or target groups 308. The example subplatforms 306 representplatform-specific (e.g., exclusive to one or more, but not all,platforms) or platform agnostic subsets of a platform (e.g., televisionand/or radio channels and/or networks, web sites, mobile device brands,etc.). Furthermore, the example platforms 304 and/or subplatforms 306may be further divided into times, time slots, days of the week, dates,and/or any other temporal division. The time slots for differentplatforms 304 and/or subplatforms 306 in the table 300 of FIG. 3 may besimilar, identical, overlapping, or different. The example target groups308 represent persons of interest for whom the effectiveness, reach,and/or performance may be determined. In the example of FIG. 3, thetarget groups 308 are not platform-specific but, instead, representpersons of interest to whom an advertiser would most prefer to directadvertising efforts via any available platform.

The example table 300 of FIG. 3 includes an effectiveness or efficiencymetric 310 (e.g., effectiveness E in equations (1) and/or (2) above) andreach 312 (e.g., reach R_(P) in Equation (2) above) of each combinationof platform 304, subplatform 306, and target group 308. The examplereach 312 data in the table 300 of FIG. 3 is expressed in millions ofpeople (e.g., a reach of “1” in Table 3 is equal to a reach of onemillion people). However, reach 312 may be expressed using any otherscale. The efficiency metrics 310 represent respective proportions orpercentages of persons in the target group 308 who are substantiallyaffected (e.g., who are persuaded, whose behavior is modified, whoseattention, emotional engagement, and/or memory retention are achieved,etc.) by the corresponding platform 304 and subplatform 306. The exampleeffectiveness determiner 104 may determine the effectiveness orefficiency metric 310 using the example equation (1).

The example table 300 of FIG. 3 includes an effectiveness or efficiencymetric 310, reach 312, and a platform metric 314 (e.g., platformperformance) of each of the combinations of platform 304, subplatform306, and target group 308. The example efficiency metric 310 of FIG. 3may be populated by the effectiveness determiner 104 of FIG. 1. Thereach 312 for the example television, online, mobile, and outdoorplatforms 304 and associated subplatforms 306 and target groups 308 maybe populated by the example television audience measurement system 108,the example online audience measurement system 110, the example mobileaudience measurement system 112, and the example outdoor audiencemeasurement system 114, respectively.

The example performance estimator 120 calculates the performance metric302 (e.g., the media impact performance metric I_(P)) for each of thecombinations of platform 304, subplatform 306, and target group 308 fromthe corresponding effectiveness 310 and reach metrics 312. For example,the performance estimator 120 of FIG. 1 calculates the product of theeffectiveness 310 and the reach values 312 as the performance metric 302for the corresponding target group 308 on a subplatform 306 of aplatform 304. The example performance estimator 120 may calculate theperformance metrics 302 using the example equation (2).

The example performance estimator 120 further determines the platformmetrics 314 (e.g., a total platform performance) for each of the exampleplatforms 304 by summing the performance metrics 302 of each of theexample subplatforms 306 and target groups 308 using the exampleequation (4) below.

$\begin{matrix}{{TP}_{P} = {\sum\limits_{S = 1}^{n}\; I_{S}}} & {{Equation}\mspace{14mu} (4)}\end{matrix}$

In the example equation (4), I_(S) is the performance metric 302 (e.g.,the media impact performance metric) for a subplatform 306 and/or targetgroup 308, TP_(P) is the platform metric 314 (e.g., the total platformperformance) for the subplatform(s) 306 and/or group(s) 308 of theselected platform P, and n is the number of subplatforms for theselected platform 304.

The example platform metric 314 represents a total performance (e.g.,considering reach 312 and effectiveness 310 on each selected subplatform306 and target group 308) for each of the example platforms 304. Theexample platform metrics 314 may be used to select between the platforms304 for an advertising campaign. In some examples, the platform metrics314 may be adjusted based on overlap statistics for the subplatforms 306and/or target groups 308.

FIG. 4 is a table 400 illustrating example calculations of costeffectiveness of a media campaign for multiple platforms. The exampletable 400 of FIG. 4 may be generated by the example performanceestimator 120 and/or the example performance-per-unit-cost estimator 122of FIG. 1 based on the data from the table 300 of FIG. 3. For example,the table 400 of FIG. 4 includes the platform 304, subplatform 306, andgroup 308 breakdowns from the table 300 of FIG. 3, as well as thecorresponding performance metrics 302.

The example table 400 further includes corresponding costs 402 (e.g.,cost(s) C_(P) of the example equation (3)) for the example platforms 304and subplatforms 306. As illustrated in FIG. 4, the cost 402 may beequal across certain target groups 308 due to the nature of a platform304 and/or a subplatform 306. For example, advertising on a particularnetwork will cost the same regardless of the target age and gendergroup. However, costs may be different between certain target groups 308(e.g., time slots and/or geographic regions in the television platform).Additionally, costs may be different for the same subplatform 306 whenmultiple dimensions of subplatform are used.

Using the performance 302 (e.g., I_(P) of the example equation (3)) andthe cost 402 (e.g., cost(s) C_(P) of the example equation (3)), theexample performance-per-unit-cost estimator 122 of FIG. 1 determines aperformance per unit cost 404 (e.g., performance per U.S. dollar, PP_(P)of the example equation (3)) for each of the combinations of platform304, subplatform 306, and target group 308. For example, theperformance-per-unit-cost estimator 122 may use the example equation (3)to calculate the performance per unit cost 404 of FIG. 4. In the exampletable 400, a higher performance per unit cost 404 represents a betteradvertisement platform 304 and/or subplatform 306, because more peoplein the target group 308 are affected by the advertisement per dollarspent on the advertisement.

The example table 400 of FIG. 4 further includes platform metrics 406(e.g., platform performance, platform costs, and platform performanceper unit cost). The example platform selector 124 of FIG. 1 may use theperformance per unit cost 404 and/or the platform metrics 406 of FIG. 4to select one or more combinations of platforms 304 and/or subplatforms306 for an advertisement. In some examples, the platform selector 124aggregates (e.g., sums, weights and sums, etc.) the performance per unitcost 404 by platform 304, subplatform 306, and/or by target group 308 toobtain an aggregate performance per unit cost.

In some examples, the platform selector 124 selects multiple platformsand/or subplatforms to reach different individuals in a universe.Because two different individuals may access different platforms and/orsubplatforms (e.g., a first individual watches a first televisionchannel at the same time a second individual watches a second televisionchannel or surfs the Internet, a first individual is exposed to mediavia a platform that a second individual is never exposed to, etc.), theexample platform selector 124 of FIG. 1 may select multiple platforms toincrease an overall effectiveness of an advertising campaign rather thansimply investing the entirety of an advertising budget into the platformand/or subplatform having the highest performance-per-unit-cost.

The example platform selector 124 of FIG. 1 may further determine acampaign effectiveness 408. The example campaign effectiveness 408 ofFIG. 4 includes the total campaign performance 410, a total campaigncost 412, and a total performance per unit cost 414. The examplecampaign effectiveness metrics 408 are based on the selection of theplatforms 304 illustrated in the example table 400 of FIG. 4. Theexample campaign performance 410 of FIG. 4 is a sum of the performancemetrics 302 of the selected platforms 304. The example performanceestimator 120 of FIG. 1 may determine the campaign performance using thefollowing equation (5).

$\begin{matrix}{P_{campaign} = {\sum\limits_{P = 1}^{m}\; {TP}_{P}}} & {{Equation}\mspace{14mu} (5)}\end{matrix}$

In equation (5), P_(campaign) is the estimated performance of thecampaign (e.g., the total campaign performance 410), TP_(P) is the totalperformance for platform P, and m is the number of platforms 304 in thecampaign.

The example campaign cost 412 is a sum of the cost 402 of the platforms304, divided by a factor of 2 to reflect that the example costs 402 areduplicated in the table 400. The example campaign performance per unitcost 414 is determined from the campaign performance 410 and thecampaign costs 412. The example performance per unit cost 414 maydetermine the campaign performance per unit cost 414 using an equationsimilar to equation (3) described above, substituting the estimatedperformance of the campaign P_(campaign) (e.g., the campaign performance410) for the platform performance PP_(P) and substituting the campaigncosts 412 for the platform cost C_(P).

Furthermore, one or more platforms 304 and/or subplatforms 306 may haveoverlapping individuals (e.g., individuals who may be counted on bothplatforms, which can artificially inflate a total reach if not correctedfor). In some examples, the platform selector 124 calculates orestimates a number of overlapping individuals between platform(s) 304,subplatform(s) 306, and/or group(s) 308 based on overlap information.The example platform selector 124 adjusts the performance metric 302and/or the performance-per-unit-cost 404 based on the overlapinformation. The example platform selector 124 selects multipleplatforms 304, subplatforms 306, and/or groups 308 based on theperformance metrics 302, the performance-per-unit-cost metric 404, theplatform metrics 406, the campaign metrics 408, and/or the overlapinformation to further improve an advertising campaign.

While example platforms, subplatforms, and target groups are illustratedin FIGS. 3 and 4, additional and/or alternative dimensions of platform,subplatform, and/or target group may be used. For example, thesubplatforms 306 of the television platform 304 may be further dividedinto particular programs, time slots, and/or geographic regions, amongother things. Additionally or alternatively, the example target groups308 may be further divided into geographic regions, income groups,and/or national origin, among other things.

The example data in the tables 300, 400 of FIGS. 3 and/or 4 are providedfor illustrative purposes only, and do not reflect actual measured data.Accordingly, the respective performances, costs, and/orperformances-per-unit-cost are examples and may change over time and/orbased on measurement methodology.

While an example manner of implementing the system 100 of FIG. 1 isillustrated in FIGS. 1 and/or 2, one or more of the elements, processesand/or devices illustrated in FIGS. 1 and/or 2 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example audience measurement system 102, the exampleeffectiveness determiner 104, the example platform metric calculator106, the example television audience measurement system 108, the exampleonline audience measurement system 110, the example mobile audiencemeasurement system 112, the example outdoor audience measurement system114, the example radio audience measurement system 116, the exampleprint media audience measurement system 118, the example performanceestimator 120, the example performance per unit cost estimator 122, theexample platform selector 124, the example program identifier 206, theexample attention evaluator 212, the example database 214, the exampletransmitter 216, the example remote data facility 220 and/or, moregenerally, the example system 100 of FIGS. 1 and/or 2 may be implementedby hardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example audiencemeasurement system 102, the example effectiveness determiner 104, theexample platform metric calculator 106, the example television audiencemeasurement system 108, the example online audience measurement system110, the example mobile audience measurement system 112, the exampleoutdoor audience measurement system 114, the example radio audiencemeasurement system 116, the example print media audience measurementsystem 118, the example performance estimator 120, the exampleperformance per unit cost estimator 122, the example platform selector124, the example program identifier 206, the example attention evaluator212, the example database 214, the example transmitter 216, the exampleremote data facility 220 and/or, more generally, the example system 100could be implemented by one or more analog or digital circuit(s), logiccircuits, programmable processor(s), application specific integratedcircuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)). When reading any of theapparatus or system claims of this patent to cover a purely softwareand/or firmware implementation, at least one of the example audiencemeasurement system 102, the example effectiveness determiner 104, theexample platform metric calculator 106, the example television audiencemeasurement system 108, the example online audience measurement system110, the example mobile audience measurement system 112, the exampleoutdoor audience measurement system 114, the example radio audiencemeasurement system 116, the example print media audience measurementsystem 118, the example performance estimator 120, the exampleperformance per unit cost estimator 122, the example platform selector124, the example program identifier 206, the example attention evaluator212, the example database 214, the example transmitter 216, the exampleremote data facility 220 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample system 100 of FIGS. 1 and/or 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 1 and/or 2, and/or may include more than one of anyor all of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the system 100 of FIGS. 1 and/or 2 are shown in FIGS. 5, 6,and 7. In this example, the machine readable instructions compriseprograms for execution by a processor such as the processor 912 shown inthe example processor platform 900 discussed below in connection withFIG. 9. The programs may be embodied in software stored on a tangiblecomputer readable storage medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), a Blu-ray disk, or a memoryassociated with the processor 912, but the entire programs and/or partsthereof could alternatively be executed by a device other than theprocessor 912 and/or embodied in firmware or dedicated hardware.Further, although the example programs are described with reference tothe flowcharts illustrated in FIGS. 5-8, many other methods ofimplementing the example system 100 may alternatively be used. Forexample, the order of execution of the blocks may be changed, and/orsome of the blocks described may be changed, eliminated, or combined.

As mentioned above, the example processes of FIGS. 5-8 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals. As used herein, “tangible computerreadable storage medium” and “tangible machine readable storage medium”are used interchangeably. Additionally or alternatively, the exampleprocesses of FIGS. 5-8 may be implemented using coded instructions(e.g., computer and/or machine readable instructions) stored on anon-transitory computer and/or machine readable medium such as a harddisk drive, a flash memory, a read-only memory, a compact disk, adigital versatile disk, a cache, a random-access memory and/or any otherstorage device or storage disk in which information is stored for anyduration (e.g., for extended time periods, permanently, for briefinstances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readabledevice or disk and to exclude propagating signals. As used herein, whenthe phrase “at least” is used as the transition term in a preamble of aclaim, it is open-ended in the same manner as the term “comprising” isopen ended.

FIG. 5 is a flowchart representative of example computer readableinstructions 500 which, when executed, cause a computer to implement theexample system 100 of FIG. 1 to determine platform (and/or subplatform)metrics. The example instructions 500 of FIG. 5 may be executed toimplement the example audience measurement systems 102, 108-118, theexample effectiveness determiner 104, the example platform metriccalculator 106, the example performance estimator 120, and/or theexample platform selector 124 of FIG. 1.

The example platform metric calculator 106 of FIG. 1 selects a firstmedia platform (e.g., the platform 304 of FIG. 3) (and/or subplatform(e.g., the subplatform 306 of FIG. 3)) (block 502). For example, theplatform metric calculator 106 may select the first media platform 304(and/or subplatform 306) from a set of platforms 304 (and/orsubplatforms 306) of interest. In some examples, selection of platforms304 and/or the platforms of interest that may be selected are determinedbased on a proposed advertising campaign. In some examples, the platformmetric calculator 106 may select from all available platforms 304 and/orsubplatforms 306, or from platforms 304 and/or subplatforms 306 forwhich reach and/or effectiveness data can be obtained. The exampleplatform metric calculator 106 selects a target group (e.g., the group308 of FIG. 3) (block 504). The target group 308 may be a distinctsubset of an audience of the selected media platform 304 (and/orsubplatform 306).

The example audience measurement systems 102, 108-118 determine reach(e.g., the reach 312 of FIG. 3, reach R_(P) in equation (2)) for theselected group 308 and selected platform 304 (and/or subplatform 306)(block 506). For example, the performance estimator 120 may requestreach 312 for the selected group 308 and selected platform 304 (and/orsubplatform 306) from a corresponding one of the example televisionaudience measurement system 108, the example online audience measurementsystem 110, the example mobile audience measurement system 112, theexample outdoor audience measurement system 114, the example radioaudience measurement system 116, and/or the example print media audiencemeasurement system 118. In some examples, block 506 may be replacedand/or modified to determine additional and/or alternative platform 304(and/or subplatform 306) metrics (e.g., ratings). The exampleeffectiveness determiner 104 of FIG. 1 determines an effectiveness orefficiency (e.g., the effectiveness metric 310 of FIG. 3, theeffectiveness metric E of equations (1) and (2)) for the selected group308 and selected platform 304 (and/or subplatform 306) (block 508). Forexample, the effectiveness determiner 104 may measure and/or referencemeasurements of neurophysiological reactions of people within or similarto the selected target group 308 to media delivered via the selectedplatform 304 (and/or subplatform 306).

The example performance estimator 120 of FIG. 1 calculates a performancemetric (e.g., the performance metric 302 of FIG. 3, the media impactperformance metric I_(P) of equations (2) and (3)) based on theeffectiveness metric 310 and the reach 312 for the selected group 308and selected platform 304 (and/or subplatform 306) (block 510). Forexample, the performance estimator 120 may determine a product of theeffectiveness metric 310 and the reach 312 to calculate the performancemetric 302. The example performance estimator 120 determines if thereare additional target groups 308 (block 512). If there are additionalgroups 308 (block 512), control returns to block 504 to select the nexttarget group 308 for the same platform 304 (and/or subplatform 306).

When there are no additional target groups 308 (block 512), the exampleperformance estimator 120 sums the performance metrics 302 for theselected platform 304 (and/or subplatform 306) to determine a platform304 (and/or subplatform 306) metric (e.g., the total platformperformance TP_(P) of equation (4)) for the selected platform 304 (block514). For example, the performance estimator 120 may determine a totalperformance for the selected platform 304 (and/or subplatform 306) toenable a comparison of the performance of the selected platform 304(and/or subplatform 306) as a whole with other platforms 304 (and/orsubplatforms 306).

The example performance estimator 120 determines whether there areadditional platforms 304 (and/or subplatforms 306) to analyze (block516). If there are additional platforms 304 (and/or subplatforms 306)(e.g., for comparison with other platforms 304 and/or subplatforms 306)to analyze (block 516), control returns to block 502 to select the nextplatform 304 (and/or subplatform 306). When there are no more platforms304 (and/or subplatforms 306) to analyze (block 516), the exampleplatform selector 124 selects one or more platforms 304 (and/orsubplatforms 306) for an advertisement campaign based on the performancemetrics 302 and/or the platform 304 (and/or subplatform 306) metrics(block 518). For example, the platform selector 124 may select one ormore platforms 304 (and/or subplatforms 306) by comparing theperformance metrics 302 and/or the platform (and/or subplatform) metricsto determine better platforms 304 (and/or subplatforms 306) foradvertising. In some examples, the platform selector 124 selectsmultiple platforms 304 and/or subplatforms 306 to compensate foroverlapping audiences. In some examples, the example platform selector124 calculates a mix (e.g., an optimized mix) of media, platforms 304,and/or subplatforms 306 based on the performance 302, the costs 402, theperformance-per-unit-cost 404, and/or one or more constraints. Exampleconstraints include lower and/or upper audience (e.g., reach) limits perplatform and/or subplatform, lower and/or upper cost limits per platformand/or subplatform. The example platform selector 124 may select betweenthe platforms 304, subplatforms 306, and/or media to increase (e.g.,maximize) a performance of the campaign and/or decrease (e.g., minimize)a cost of an advertising campaign within the constraints set on each ofthe platforms 304, subplatforms 306, and/or media under consideration.In some examples, the platform selector 124 outputs a list of selectedplatforms 304 (and/or subplatforms 306) as a recommendation forinforming an advertising campaign. The example instructions 500 may thenend.

FIG. 6 is a flowchart representative of example computer readableinstructions 600 which, when executed, cause a computer to implement theexample system 100 of FIG. 1 to determine platform (e.g., the platforms304 of FIG. 4) (and/or subplatform (e.g., the subplatforms 306 of FIG.4)) performance per unit cost (e.g., the performance per unit cost 404of FIG. 4) for a media campaign. The example instructions 600 of FIG. 6may be executed to implement the example platform metric calculator 106,the example performance estimator 120, the example performance per unitcost estimator 122, and/or the example platform selector 124 of FIG. 1.

The example performance per unit cost estimator 122 of FIG. 1 obtains(e.g., receives, accesses from storage) platform 304 (and/or subplatform306) metrics (e.g., the media impact performance metric I_(P) ofequation (3), the total platform performance TP_(P) of equation (4)) forplatforms 304 (and/or subplatforms 306) of interest (block 602). Forexample, the performance per unit cost estimator 122 may obtainperformance metrics 302 from the performance estimator 120 of FIG. 1(e.g., based on effectiveness metrics 310 and reach 312 of the platform304 and/or subplatform 306 of FIG. 3). The example performance per unitcost estimator 122 obtains (e.g., receives, accesses from storage) adcampaign costs (e.g., costs 404 of FIG. 4) for the platforms 304 (and/orsubplatforms 306) of interest (block 604). Costs 404 may be obtained viamanual data entry and/or stored on a storage device for retrieval by theexample performance per unit cost estimator 122.

The example performance per unit cost estimator 122 selects a mediaplatform 304 (and/or subplatform 306) of interest (block 606). Forexample, the performance per unit cost estimator 122 may select a firstmedia platform 304 (and/or subplatform 306) from a set of platforms 304(and/or subplatforms 306) of interest. The example performance per unitcost estimator 122 divides the performance metric 302 for the selectedplatform 304 (and/or subplatform 306) by a cost (e.g., a cost ofadvertising) for the selected platform 304 (and/or subplatform 306) toobtain a platform 304 (and/or subplatform 306) performance per unit cost404 (e.g., the platform performance per unit cost (PP_(P))) (block 608).The example performance per unit cost 404 may be expressed in units ofperformance per dollar or any other currency or unit of cost.

The example performance per unit cost estimator 122 determines whetherthere are additional platforms 304 (and/or subplatforms 306) of interestto be analyzed (block 610). If there are additional platforms 304(and/or subplatforms 306) to be analyzed (block 610), control returns toblock 606 to select another platform 304 (and/or subplatform 306). Whenthere are no additional platforms 304 (and/or subplatforms 306) to beanalyzed (block 610), the example platform selector 124 of FIG. 1selects a desired number of highest performance per unit cost platforms(and/or subplatforms) (block 612). For example, the platform selector124 may select a set of platforms 304 (and/or subplatforms 306) on whichan advertising campaign may be most effectively implemented. Theselected set of platforms 304 (and/or subplatforms 306) may be output asa recommendation or selection for informing the advertising campaign. Insome examples, the platform selector 124 selects and/or outputs therecommendation or selection of platforms 304 and/or subplatforms 306 tocompensate for overlapping audiences (e.g., based on overlapinformation). The example instructions 600 of FIG. 6 then end.

FIG. 7 is a flowchart representative of example computer readableinstructions 700 which, when executed, cause a computer to implement theexample system 100 of FIG. 1 to estimate performance of a campaign. Theexample instructions 700 of FIG. 7 may be executed to implement theexample audience measurement systems 102, 108-118, the exampleeffectiveness determiner 104, the example platform metric calculator106, and/or the example performance estimator 120 of FIG. 1.

The example performance estimator 120 of FIG. 1 obtains (e.g., receives,accesses from storage) effectiveness metrics for platforms of anadvertising campaign (e.g., the example platform metrics 406 for theexample platforms 304 of FIG. 4, the media impact performance metricI_(P) of equation (3)) (block 702). For example, the performanceestimator 120 may receive a description of an advertising campaign(e.g., a listing of platforms 304 and/or subplatforms 306) and requesteffectiveness metrics (e.g., the example effectiveness metrics 310 ofFIG. 3) of the described platforms (e.g., from the effectivenessdeterminer 104 of FIG. 1). The example performance estimator 120 obtains(e.g., receives, accesses from storage) reach (e.g., the example reach312 of FIG. 3, the reach R_(P) of equation (2)) for the platforms of theadvertising campaign (block 704). For example, the performance estimator120 requests reach 312 for each of the described platforms 304 from theaudience measurement system 102 and/or from the platform-specificaudience measurement systems 108-118.

The example performance estimator 120 selects a first platform 304 ofthe advertising campaign (block 706). The performance estimator 120calculates a platform metric (e.g., a performance metric 314, the totalplatform performance TP_(P) of equation (4)) for the selected platform304 based on the effectiveness metric 310 and the reach 312 (block 708).The platform metric 314 therefore represents an estimate of theperformance of the platform 304 (e.g., an estimate of the performance ofa portion of the advertising campaign). The performance estimator 120determines whether there are additional platforms 304 in the advertisingcampaign to be analyzed (block 710) and, if so, returns to block 706 toselect another platform 304 to be analyzed.

When there are no further platforms 304 to be analyzed (block 710), theexample performance estimator 120 sums the platform metrics 304 of theplatforms of the advertising campaign to estimate the overallperformance of the campaign (e.g., the campaign performance 410 of FIG.4) (block 712). For example, by summing the platform metrics 314 (thatrepresent the individual platforms) for all platforms 304 that are usedin the campaign, the example performance estimator 120 may estimate atotal or overall performance 410 of the campaign (e.g., the estimatedperformance of the campaign P_(campaign)). In some examples, theperformance 410 for the campaign represents an estimated number ofpeople affected or influenced by the campaign, where a higherperformance affects more people.

The example performance estimator 120 determines whether there arechanges to the ad campaign (block 714). If there are changes (block714), control returns to block 702 to calculate a performance of themodified campaign. If there are no changes (block 714), the exampleinstructions 700 end.

FIG. 8 is a flowchart illustrating example machine readable instructions800 which may be executed to analyze EEG data collected from the exampleheadset 202 and implemented by the example effectiveness determiner 104of FIG. 2. The example headset 202 has a plurality of electrodes thatcontact the scalp of a subject to receive electrical signals from thesubject's brain. The example instructions 800 for analyzing EEG dataincludes reading the EEG signals from the electrodes of the EEG sensor204 (block 802). In the illustrated example, the signals are convertedfrom an analog signal to a digital signal (block 804). In some examples,the analog-to-digital conversion takes place in a processing unit, suchas, for example, a remote processor for example at the example remotedata facility 220. In other examples, the analog-to-digital conversiontakes place adjacent the electrodes within the headset 202 to convertthe signal as close to the source as possible.

In the illustrated example, the signals are conditioned (block 806) toimprove the usefulness of the signals and the accessibility of the datacontained therein. For example, the conditioning may include amplifyingthe signals and/or filtering the signals (e.g., with a band passfilter).

The signals are analyzed (block 808) to, for example, determine a mentalstate of the subject, an engagement with media as an audience member,and/or otherwise in accordance with the teachings of this disclosure.For example, the signals may be analyzed by the attention evaluator 812as disclosed above to determine and/or calculate region(s) of brainactivity, interaction(s) between regions of brain activity, frequencyinteraction(s), frequency coupling(s), inter-regional coherencies offrequency band, gain(s), phase(s), inter-hemispheric measurement(s),asymmetry in one or more frequency band(s), inter-hemispheric asymmetry,asymmetry in inter-regional intra-hemispheric coherence, asymmetry ininter-regional intra-hemispheric inter-frequency coupling, pattern(s) ofoscillation in frequency band(s), degree(s) of phase synchrony betweenoscillation patterns, repeating sequence(s) of relative phase anglesbetween oscillation patterns, amplitude difference(s) and/or othercalculations.

In the illustrated example, the signals are transmitted to an output(block 810), such as, for example, by the transmitter 216 of the exampleeffectiveness determiner 104. In addition, the output may include thewired or wireless communications detailed herein. After the output(block 810), the example instructions 800 ends.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIGS. 5, 6, 7, and/or 8 to implementthe system 100 of FIGS. 1 and/or 2. The processor platform 900 can be,for example, a server, a personal computer, a mobile device (e.g., acell phone, a smart phone, a tablet such as an iPad™), or any other typeof computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 920 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIGS. 5, 6, 7, and/or 8 may be stored inthe mass storage device 928, in the volatile memory 914, in thenon-volatile memory 916, and/or on a removable tangible computerreadable storage medium such as a CD or DVD.

Methods and apparatus disclosed herein advantageously enablerationalization and/or comparison of media campaigns and/or ratingsacross different media platforms. Example methods and apparatusdisclosed herein enable the estimation and/or prediction of performanceof a media campaign and/or can be used to improve or even optimizecross-platform campaigns (e.g., optimization of expenditures betweendifferent media platforms), thereby increasing return-on-investment foradvertisers.

Example methods and apparatus disclosed herein use a media impactperformance metric to take into account the differing levels ofengagement, attention and, more generally, effectiveness or efficiencyof different media platforms. Example methods and apparatus disclosedherein further use the media impact performance metric to comparedifferent media platforms to, for example, determine an improved (e.g.,optimal) mix of platforms on which to advertise to make efficient andeffective use of an advertising campaign budget.

To the extent any of the documents incorporated by reference hereinconflict with this disclosure, this disclosure is considered to control.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus, comprising: a sensor to measurefirst neuro-response data from a person when exposed to media output viaa first media delivery platform type, the sensor to measure secondneuro-response data from the person when exposed to media output via asecond media delivery platform type, the first media delivery platformtype being different than the second media delivery platform type; aperformance estimator to: calculate a first performance metric based ona first reach of the first media delivery platform type with respect toa target group of audience members and a first effectiveness metric forthe first media delivery platform type, the first effectiveness metricbased on the first neuro-response data; and calculate a secondperformance metric based on a second reach of the second media deliveryplatform type with respect to the target group of audience members and asecond effectiveness metric for the second media delivery platform type,the second effectiveness metric based on the second neuro-response data;and a platform selector to rate at least one of the first media deliveryplatform type or the second media delivery platform type based on thefirst and the second performance metrics, at least one of theperformance estimator, the sensor or the platform selector includinghardware.
 2. The apparatus as defined in claim 1, further including aperformance per unit cost estimator to: access a first ad campaign costfor the first media delivery platform type and a second ad campaign costfor the second media delivery platform type; calculate a first platformperformance per unit cost for the first media delivery platform typebased on the first performance metric and the first ad campaign cost;and calculate a second platform performance per unit cost for the secondmedia delivery platform type based on the second performance metric andthe second ad campaign cost; and wherein the platform selector isfurther to select the first media delivery platform type or the secondmedia delivery platform type for an ad campaign based on the firstplatform performance per unit cost and the second platform performanceper unit cost.
 3. The apparatus as defined in claim 1, wherein theperson is representative of the target group.
 4. The apparatus asdefined in claim 1, wherein the first effectiveness metric measures atleast one of: engagement, attention, memory, persuasion, effectiveness,emotion, or purchase intent.
 5. The apparatus as defined in claim 1,wherein the performance estimator estimates a total performance of amedia campaign based on the first and second performance metrics.
 6. Theapparatus as defined in claim 1, wherein the first media deliveryplatform type is one of a television platform, a mobile platform, anonline platform, an outdoor platform, a radio platform or a print mediaplatform.
 7. The apparatus as defined in claim 1, wherein the firstperformance metric includes a product of the first effectiveness metricand the first reach.
 8. A tangible computer readable storage mediumcomprising computer readable instructions which, when executed by aprocessor, cause the processor to at least: measure, with a sensor,first neuro-response data from a person when exposed to media output viaa first media delivery platform type, measure, with the sensor, secondneuro-response data from the person when exposed to media output via asecond media delivery platform type, the first media delivery platformtype being different than the second media delivery platform type;calculate a first performance metric based on a first reach of the firstmedia delivery platform type with respect to a target group of audiencemembers and a first effectiveness metric for the first media deliveryplatform type, the first effectiveness metric based on the firstneuro-response data; and calculate a second performance metric based ona second reach of the second media delivery platform type with respectto the target group of audience members and a second effectivenessmetric for the second media delivery platform type, the secondeffectiveness metric based on the second neuro-response data; and rateat least one of the first media delivery platform type or the secondmedia delivery platform type based on the first and the secondperformance metrics.
 9. The tangible computer readable storage medium asdefined in claim 8, wherein the instructions cause the processor to:access a first ad campaign cost for the first media delivery platformtype and a second ad campaign cost for the second media deliveryplatform type; calculate a first platform performance per unit cost forthe first media delivery platform type based on the first performancemetric and the first ad campaign cost; and calculate a second platformperformance per unit cost for the second media delivery platform typebased on the second performance metric and the second ad campaign cost;and select the first media delivery platform type or the second mediadelivery platform type for an ad campaign based on the first platformperformance per unit cost and the second platform performance per unitcost.
 10. The tangible computer readable storage medium as defined inclaim 8, wherein the person is representative of the target group. 11.The tangible computer readable storage medium as defined in claim 8,wherein the first effectiveness metric measures at least one of:engagement, attention, memory, persuasion, effectiveness, emotion, orpurchase intent.
 12. The tangible computer readable storage medium asdefined in claim 8, wherein the instructions cause the processor toestimate a total performance of a media campaign based on the first andsecond performance metrics.
 13. The tangible computer readable storagemedium as defined in claim 8, wherein the first media delivery platformtype is one of a television platform, a mobile platform, an onlineplatform, an outdoor platform, a radio platform or a print mediaplatform.
 14. The tangible computer readable storage medium as definedin claim 8, wherein the first performance metric includes a product ofthe first effectiveness metric and the first reach.
 15. A method,comprising: measuring, with a sensor, first neuro-response data from aperson when exposed to media output via a first media delivery platformtype; measuring, with the sensor, second neuro-response data from theperson when exposed to media output via a second media delivery platformtype, the first media delivery platform type being different than thesecond media delivery platform type; accessing, by executing aninstruction with a processor, a first performance metric for the firstmedia delivery platform type and a second performance metric for thesecond media delivery platform type, the first performance metric basedon a first reach of the first media delivery platform type with respectto a target group of audience members and a first effectiveness metricfor the first media delivery platform type, the first effectivenessmetric based on the first neuro-response data, the second performancemetric based on a second reach of the second media delivery platformtype with respect to the target group and a second effectiveness metricfor the second media delivery platform type, the second effectivenessmetric based on the second neuro-response data; and rating, by executingan instruction with the processor, at least one of the first mediadelivery platform type or the second media delivery platform type basedon the first and the second platform metrics.
 16. The method as definedin claim 15, further including: accessing, by executing an instructionwith the processor, a first ad campaign cost for the first mediadelivery platform type and a second ad campaign cost for the secondmedia delivery platform type; calculating, by executing an instructionwith the processor, a first platform performance per unit cost for thefirst media delivery platform type based on the first performance metricand the first ad campaign cost; calculating, by executing an instructionwith the processor, a second platform performance per unit cost for thesecond media delivery platform type based on the second performancemetric and the second ad campaign cost; selecting, by executing aninstruction with the processor, the first media delivery platform typeor the second media delivery platform type for an ad campaign based onthe first platform performance per unit cost and the second platformperformance per unit cost.
 17. The method as defined in claim 15,wherein the person is representative of the target group.
 18. The methodas defined in claim 15, wherein the first effectiveness metric measuresat least one of: engagement, attention, memory, persuasion,effectiveness, emotion, or purchase intent.
 19. The method as defined inclaim 15, further including estimating, by executing an instruction withthe processor, a total performance of a media campaign based on thefirst and second performance metrics.
 20. The method as defined in claim15, wherein the first media delivery platform type is one of atelevision platform, a mobile platform, an online platform, an outdoorplatform, a radio platform or a print media platform.